Overview

Dataset statistics

Number of variables20
Number of observations1763
Missing cells821
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory289.2 KiB
Average record size in memory168.0 B

Variable types

Numeric15
Categorical5

Alerts

valido1 has constant value ""Constant
valido2 has constant value ""Constant
anio is highly overall correlated with cursoHigh correlation
curso is highly overall correlated with anio and 1 other fieldsHigh correlation
p_ext is highly overall correlated with salaHigh correlation
sala is highly overall correlated with p_extHigh correlation
pa1_prom is highly overall correlated with abandona2_p and 2 other fieldsHigh correlation
pa2_prom is highly overall correlated with aprueba_p and 1 other fieldsHigh correlation
prom_edad is highly overall correlated with cursoHigh correlation
abandona1_p is highly overall correlated with aprueba_pHigh correlation
abandona2_p is highly overall correlated with pa1_prom and 2 other fieldsHigh correlation
aprueba_p is highly overall correlated with pa1_prom and 4 other fieldsHigh correlation
aprueba_rel_p is highly overall correlated with pa1_prom and 3 other fieldsHigh correlation
final_prom has 821 (46.6%) missing valuesMissing
curso has unique valuesUnique
p_ext has 54 (3.1%) zerosZeros
p_recursa has 222 (12.6%) zerosZeros
abandona2_p has 154 (8.7%) zerosZeros

Reproduction

Analysis started2023-07-29 18:53:27.567695
Analysis finished2023-07-29 18:53:58.692046
Duration31.12 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

anio
Real number (ℝ)

Distinct9
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.9285
Minimum2011
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2023-07-29T15:53:58.759067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12013
median2015
Q32017
95-th percentile2019
Maximum2019
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6569886
Coefficient of variation (CV)0.0013186515
Kurtosis-1.3223427
Mean2014.9285
Median Absolute Deviation (MAD)2
Skewness0.10407045
Sum3552319
Variance7.0595885
MonotonicityIncreasing
2023-07-29T15:53:58.888567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2013 239
13.6%
2014 219
12.4%
2012 218
12.4%
2019 217
12.3%
2018 209
11.9%
2011 193
10.9%
2017 184
10.4%
2015 150
8.5%
2016 134
7.6%
ValueCountFrequency (%)
2011 193
10.9%
2012 218
12.4%
2013 239
13.6%
2014 219
12.4%
2015 150
8.5%
2016 134
7.6%
2017 184
10.4%
2018 209
11.9%
2019 217
12.3%
ValueCountFrequency (%)
2019 217
12.3%
2018 209
11.9%
2017 184
10.4%
2016 134
7.6%
2015 150
8.5%
2014 219
12.4%
2013 239
13.6%
2012 218
12.4%
2011 193
10.9%

cuat
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.5 KiB
1
917 
2
846 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1763
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 917
52.0%
2 846
48.0%

Length

2023-07-29T15:53:59.036695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-29T15:53:59.159199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 917
52.0%
2 846
48.0%

Most occurring characters

ValueCountFrequency (%)
1 917
52.0%
2 846
48.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1763
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 917
52.0%
2 846
48.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1763
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 917
52.0%
2 846
48.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1763
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 917
52.0%
2 846
48.0%

SEDE
Real number (ℝ)

Distinct21
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6097561
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2023-07-29T15:53:59.259058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile28
Maximum42
Range41
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.3626484
Coefficient of variation (CV)1.2651977
Kurtosis5.5677577
Mean6.6097561
Median Absolute Deviation (MAD)2
Skewness2.425328
Sum11653
Variance69.933889
MonotonicityNot monotonic
2023-07-29T15:53:59.360253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2 470
26.7%
1 325
18.4%
4 210
11.9%
10 207
11.7%
5 192
10.9%
6 134
 
7.6%
15 56
 
3.2%
28 50
 
2.8%
39 26
 
1.5%
14 22
 
1.2%
Other values (11) 71
 
4.0%
ValueCountFrequency (%)
1 325
18.4%
2 470
26.7%
4 210
11.9%
5 192
10.9%
6 134
 
7.6%
10 207
11.7%
13 3
 
0.2%
14 22
 
1.2%
15 56
 
3.2%
21 12
 
0.7%
ValueCountFrequency (%)
42 8
 
0.5%
41 1
 
0.1%
39 26
1.5%
35 4
 
0.2%
34 5
 
0.3%
33 8
 
0.5%
32 11
 
0.6%
31 9
 
0.5%
30 3
 
0.2%
28 50
2.8%

MATERIA
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.5 KiB
53
1155 
3
608 

Length

Max length2
Median length2
Mean length1.6551333
Min length1

Characters and Unicode

Total characters2918
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
53 1155
65.5%
3 608
34.5%

Length

2023-07-29T15:53:59.472293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-29T15:53:59.592918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
53 1155
65.5%
3 608
34.5%

Most occurring characters

ValueCountFrequency (%)
3 1763
60.4%
5 1155
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2918
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1763
60.4%
5 1155
39.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2918
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1763
60.4%
5 1155
39.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1763
60.4%
5 1155
39.6%

curso
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1763
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1281.3233
Minimum0
Maximum2663
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2023-07-29T15:53:59.708354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile93.6
Q1604.5
median1171
Q32032.5
95-th percentile2527.9
Maximum2663
Range2663
Interquartile range (IQR)1428

Descriptive statistics

Standard deviation797.3445
Coefficient of variation (CV)0.62228205
Kurtosis-1.3001436
Mean1281.3233
Median Absolute Deviation (MAD)702
Skewness0.13751649
Sum2258973
Variance635758.24
MonotonicityNot monotonic
2023-07-29T15:53:59.856463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.1%
1768 1
 
0.1%
1805 1
 
0.1%
1864 1
 
0.1%
1772 1
 
0.1%
1793 1
 
0.1%
1766 1
 
0.1%
1897 1
 
0.1%
1785 1
 
0.1%
1771 1
 
0.1%
Other values (1753) 1753
99.4%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
ValueCountFrequency (%)
2663 1
0.1%
2662 1
0.1%
2660 1
0.1%
2647 1
0.1%
2646 1
0.1%
2645 1
0.1%
2644 1
0.1%
2643 1
0.1%
2642 1
0.1%
2641 1
0.1%

turno
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size27.5 KiB
C
611 
B
490 
A
447 
D
215 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1763
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowB
5th rowC

Common Values

ValueCountFrequency (%)
C 611
34.7%
B 490
27.8%
A 447
25.4%
D 215
 
12.2%

Length

2023-07-29T15:53:59.983412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-29T15:54:00.111507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
c 611
34.7%
b 490
27.8%
a 447
25.4%
d 215
 
12.2%

Most occurring characters

ValueCountFrequency (%)
C 611
34.7%
B 490
27.8%
A 447
25.4%
D 215
 
12.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1763
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 611
34.7%
B 490
27.8%
A 447
25.4%
D 215
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 1763
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 611
34.7%
B 490
27.8%
A 447
25.4%
D 215
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1763
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 611
34.7%
B 490
27.8%
A 447
25.4%
D 215
 
12.2%

n_alum
Real number (ℝ)

Distinct174
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.255247
Minimum1
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2023-07-29T15:54:00.242399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile36
Q166
median89
Q3113
95-th percentile147
Maximum205
Range204
Interquartile range (IQR)47

Descriptive statistics

Standard deviation33.967845
Coefficient of variation (CV)0.37635313
Kurtosis-0.30205805
Mean90.255247
Median Absolute Deviation (MAD)24
Skewness0.069540559
Sum159120
Variance1153.8145
MonotonicityNot monotonic
2023-07-29T15:54:00.391057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89 34
 
1.9%
98 31
 
1.8%
86 30
 
1.7%
84 28
 
1.6%
92 26
 
1.5%
81 26
 
1.5%
85 25
 
1.4%
78 24
 
1.4%
83 24
 
1.4%
97 22
 
1.2%
Other values (164) 1493
84.7%
ValueCountFrequency (%)
1 3
0.2%
2 1
 
0.1%
3 1
 
0.1%
5 1
 
0.1%
7 1
 
0.1%
8 2
0.1%
11 1
 
0.1%
12 1
 
0.1%
13 1
 
0.1%
14 4
0.2%
ValueCountFrequency (%)
205 1
0.1%
202 1
0.1%
191 1
0.1%
190 1
0.1%
184 1
0.1%
177 1
0.1%
175 1
0.1%
174 1
0.1%
173 1
0.1%
172 1
0.1%

p_ext
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct999
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1437736
Minimum0
Maximum1
Zeros54
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2023-07-29T15:54:00.550547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.017241379
Q10.064051282
median0.12371134
Q30.19462482
95-th percentile0.34563492
Maximum1
Range1
Interquartile range (IQR)0.13057354

Descriptive statistics

Standard deviation0.10769989
Coefficient of variation (CV)0.74909366
Kurtosis4.2704293
Mean0.1437736
Median Absolute Deviation (MAD)0.064694457
Skewness1.4844007
Sum253.47285
Variance0.011599266
MonotonicityNot monotonic
2023-07-29T15:54:00.700897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54
 
3.1%
0.1666666667 18
 
1.0%
0.1111111111 18
 
1.0%
0.07692307692 14
 
0.8%
0.1428571429 14
 
0.8%
0.09090909091 12
 
0.7%
0.0625 12
 
0.7%
0.09523809524 11
 
0.6%
0.04 9
 
0.5%
0.03125 9
 
0.5%
Other values (989) 1592
90.3%
ValueCountFrequency (%)
0 54
3.1%
0.006993006993 1
 
0.1%
0.008474576271 1
 
0.1%
0.008620689655 1
 
0.1%
0.009900990099 1
 
0.1%
0.01 1
 
0.1%
0.0101010101 1
 
0.1%
0.01111111111 1
 
0.1%
0.01123595506 1
 
0.1%
0.01149425287 1
 
0.1%
ValueCountFrequency (%)
1 1
0.1%
0.7692307692 1
0.1%
0.7142857143 1
0.1%
0.619047619 1
0.1%
0.6181818182 1
0.1%
0.5555555556 1
0.1%
0.5542168675 1
0.1%
0.5384615385 1
0.1%
0.5346534653 1
0.1%
0.5338983051 1
0.1%

p_recursa
Real number (ℝ)

Distinct1162
Distinct (%)65.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29344996
Minimum0
Maximum0.87142857
Zeros222
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2023-07-29T15:54:00.852538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.047619048
median0.304
Q30.48412298
95-th percentile0.65982278
Maximum0.87142857
Range0.87142857
Interquartile range (IQR)0.43650394

Descriptive statistics

Standard deviation0.22585161
Coefficient of variation (CV)0.76964269
Kurtosis-1.2231728
Mean0.29344996
Median Absolute Deviation (MAD)0.21773913
Skewness0.14631208
Sum517.35227
Variance0.051008952
MonotonicityNot monotonic
2023-07-29T15:54:00.996448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 222
 
12.6%
0.3333333333 22
 
1.2%
0.5 18
 
1.0%
0.6 7
 
0.4%
0.3636363636 7
 
0.4%
0.4285714286 5
 
0.3%
0.6363636364 5
 
0.3%
0.1818181818 5
 
0.3%
0.2 5
 
0.3%
0.05 5
 
0.3%
Other values (1152) 1462
82.9%
ValueCountFrequency (%)
0 222
12.6%
0.006172839506 1
 
0.1%
0.006289308176 2
 
0.1%
0.006493506494 1
 
0.1%
0.007575757576 1
 
0.1%
0.007874015748 1
 
0.1%
0.007936507937 1
 
0.1%
0.008 1
 
0.1%
0.008130081301 1
 
0.1%
0.00826446281 1
 
0.1%
ValueCountFrequency (%)
0.8714285714 1
 
0.1%
0.8113207547 1
 
0.1%
0.7916666667 1
 
0.1%
0.7763157895 1
 
0.1%
0.7702702703 1
 
0.1%
0.7692307692 2
0.1%
0.7583892617 1
 
0.1%
0.7558139535 1
 
0.1%
0.75 3
0.2%
0.7432432432 1
 
0.1%

sala
Real number (ℝ)

Distinct146
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.239365
Minimum0
Maximum187
Zeros16
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2023-07-29T15:54:01.144528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q118
median35
Q376
95-th percentile133
Maximum187
Range187
Interquartile range (IQR)58

Descriptive statistics

Standard deviation41.638075
Coefficient of variation (CV)0.82879382
Kurtosis0.66564885
Mean50.239365
Median Absolute Deviation (MAD)25
Skewness1.0585169
Sum88572
Variance1733.7293
MonotonicityNot monotonic
2023-07-29T15:54:01.288094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 83
 
4.7%
10 74
 
4.2%
15 72
 
4.1%
5 68
 
3.9%
4 59
 
3.3%
84 48
 
2.7%
24 48
 
2.7%
26 40
 
2.3%
25 37
 
2.1%
86 34
 
1.9%
Other values (136) 1200
68.1%
ValueCountFrequency (%)
0 16
 
0.9%
1 12
 
0.7%
2 4
 
0.2%
3 8
 
0.5%
4 59
3.3%
5 68
3.9%
6 5
 
0.3%
7 20
 
1.1%
8 8
 
0.5%
9 12
 
0.7%
ValueCountFrequency (%)
187 2
 
0.1%
186 6
0.3%
185 1
 
0.1%
181 1
 
0.1%
180 5
0.3%
179 12
0.7%
178 3
 
0.2%
177 5
0.3%
165 2
 
0.1%
164 2
 
0.1%

pa1_prom
Real number (ℝ)

Distinct1487
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6846082
Minimum1.3484848
Maximum7.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2023-07-29T15:54:01.443768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.3484848
5-th percentile2.2973599
Q13.0288889
median3.627907
Q34.2465353
95-th percentile5.2967342
Maximum7.8
Range6.4515152
Interquartile range (IQR)1.2176464

Descriptive statistics

Standard deviation0.91726918
Coefficient of variation (CV)0.24894619
Kurtosis0.40999226
Mean3.6846082
Median Absolute Deviation (MAD)0.61228198
Skewness0.49405063
Sum6495.9642
Variance0.84138275
MonotonicityNot monotonic
2023-07-29T15:54:01.586151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 16
 
0.9%
3.5 11
 
0.6%
3 10
 
0.6%
2.5 9
 
0.5%
5 8
 
0.5%
6 6
 
0.3%
3.25 6
 
0.3%
2.625 5
 
0.3%
2 5
 
0.3%
3.4 5
 
0.3%
Other values (1477) 1682
95.4%
ValueCountFrequency (%)
1.348484848 1
0.1%
1.43902439 1
0.1%
1.509090909 1
0.1%
1.61038961 1
0.1%
1.625 1
0.1%
1.636363636 1
0.1%
1.666666667 1
0.1%
1.738095238 1
0.1%
1.803921569 1
0.1%
1.807692308 1
0.1%
ValueCountFrequency (%)
7.8 1
0.1%
7.333333333 1
0.1%
7.206896552 1
0.1%
7.1 1
0.1%
7 1
0.1%
6.625 1
0.1%
6.5 1
0.1%
6.444444444 1
0.1%
6.4 1
0.1%
6.333333333 1
0.1%

pa2_prom
Real number (ℝ)

Distinct1326
Distinct (%)75.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2222866
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2023-07-29T15:54:01.739615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.7905263
Q13.6179144
median4.2115385
Q34.7927847
95-th percentile5.578884
Maximum10
Range9
Interquartile range (IQR)1.1748703

Descriptive statistics

Standard deviation0.91053243
Coefficient of variation (CV)0.21564913
Kurtosis2.3499526
Mean4.2222866
Median Absolute Deviation (MAD)0.58748115
Skewness0.45897309
Sum7443.8913
Variance0.82906931
MonotonicityNot monotonic
2023-07-29T15:54:01.882049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 20
 
1.1%
5 12
 
0.7%
3.5 11
 
0.6%
4.428571429 9
 
0.5%
4.166666667 8
 
0.5%
4.5 8
 
0.5%
3 8
 
0.5%
4.25 6
 
0.3%
4.375 5
 
0.3%
4.2 5
 
0.3%
Other values (1316) 1671
94.8%
ValueCountFrequency (%)
1 1
0.1%
1.236363636 1
0.1%
1.409090909 1
0.1%
1.542857143 1
0.1%
1.71875 1
0.1%
1.75 2
0.1%
1.769230769 1
0.1%
1.866666667 2
0.1%
1.894736842 1
0.1%
1.916666667 1
0.1%
ValueCountFrequency (%)
10 1
0.1%
9.285714286 1
0.1%
9 2
0.1%
8 2
0.1%
7.763157895 1
0.1%
7.078125 1
0.1%
6.909090909 1
0.1%
6.875 2
0.1%
6.787234043 1
0.1%
6.78 1
0.1%

final_prom
Real number (ℝ)

Distinct509
Distinct (%)54.0%
Missing821
Missing (%)46.6%
Infinite0
Infinite (%)0.0%
Mean3.9538137
Minimum1.59375
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2023-07-29T15:54:02.027600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.59375
5-th percentile2.477381
Q13.3181818
median4
Q34.5802207
95-th percentile5.3995652
Maximum8
Range6.40625
Interquartile range (IQR)1.2620389

Descriptive statistics

Standard deviation0.93089863
Coefficient of variation (CV)0.23544322
Kurtosis0.50188347
Mean3.9538137
Median Absolute Deviation (MAD)0.63157895
Skewness0.2767742
Sum3724.4925
Variance0.86657226
MonotonicityNot monotonic
2023-07-29T15:54:02.516458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 34
 
1.9%
3 21
 
1.2%
3.5 15
 
0.9%
4.5 13
 
0.7%
2 13
 
0.7%
5 13
 
0.7%
4.666666667 11
 
0.6%
4.333333333 10
 
0.6%
3.333333333 9
 
0.5%
2.5 9
 
0.5%
Other values (499) 794
45.0%
(Missing) 821
46.6%
ValueCountFrequency (%)
1.59375 1
 
0.1%
1.666666667 1
 
0.1%
1.8 1
 
0.1%
1.928571429 1
 
0.1%
1.954545455 1
 
0.1%
2 13
0.7%
2.066666667 1
 
0.1%
2.090909091 1
 
0.1%
2.111111111 1
 
0.1%
2.125 1
 
0.1%
ValueCountFrequency (%)
8 1
0.1%
7.552631579 1
0.1%
7.5 1
0.1%
7 2
0.1%
6.666666667 1
0.1%
6.571428571 1
0.1%
6.55 1
0.1%
6.511627907 1
0.1%
6.5 1
0.1%
6.333333333 2
0.1%

prom_edad
Real number (ℝ)

Distinct1355
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2986158
Minimum2
Maximum4.4857143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2023-07-29T15:54:02.667238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.9427663
Q13.1079756
median3.244186
Q33.4528302
95-th percentile3.8519957
Maximum4.4857143
Range2.4857143
Interquartile range (IQR)0.34485457

Descriptive statistics

Standard deviation0.2942168
Coefficient of variation (CV)0.08919402
Kurtosis1.3028687
Mean3.2986158
Median Absolute Deviation (MAD)0.15618605
Skewness0.66167983
Sum5815.4597
Variance0.086563528
MonotonicityNot monotonic
2023-07-29T15:54:02.820544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 23
 
1.3%
3.25 10
 
0.6%
3.333333333 10
 
0.6%
3.428571429 8
 
0.5%
3.4 6
 
0.3%
3.6 6
 
0.3%
3.181818182 6
 
0.3%
3.5 6
 
0.3%
3.125 5
 
0.3%
3.166666667 5
 
0.3%
Other values (1345) 1678
95.2%
ValueCountFrequency (%)
2 1
0.1%
2.403100775 1
0.1%
2.487179487 1
0.1%
2.495495495 1
0.1%
2.5 1
0.1%
2.509803922 1
0.1%
2.528735632 1
0.1%
2.531468531 1
0.1%
2.542253521 1
0.1%
2.548780488 1
0.1%
ValueCountFrequency (%)
4.485714286 1
0.1%
4.413793103 1
0.1%
4.405405405 1
0.1%
4.396825397 1
0.1%
4.392156863 1
0.1%
4.391752577 1
0.1%
4.375 1
0.1%
4.310344828 1
0.1%
4.277777778 1
0.1%
4.270833333 1
0.1%

abandona1_p
Real number (ℝ)

Distinct1164
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32516494
Minimum0
Maximum0.99145299
Zeros8
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2023-07-29T15:54:02.973451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.10585897
Q10.21503295
median0.30952381
Q30.418486
95-th percentile0.60222245
Maximum0.99145299
Range0.99145299
Interquartile range (IQR)0.20345305

Descriptive statistics

Standard deviation0.15444489
Coefficient of variation (CV)0.47497398
Kurtosis1.2710465
Mean0.32516494
Median Absolute Deviation (MAD)0.1
Skewness0.75578817
Sum573.26579
Variance0.023853223
MonotonicityNot monotonic
2023-07-29T15:54:03.116453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3333333333 22
 
1.2%
0.2 12
 
0.7%
0.5 11
 
0.6%
0.4 10
 
0.6%
0.25 9
 
0.5%
0.3636363636 9
 
0.5%
0 8
 
0.5%
0.125 7
 
0.4%
0.347826087 7
 
0.4%
0.2142857143 7
 
0.4%
Other values (1154) 1661
94.2%
ValueCountFrequency (%)
0 8
0.5%
0.01176470588 1
 
0.1%
0.01298701299 1
 
0.1%
0.01941747573 1
 
0.1%
0.02040816327 1
 
0.1%
0.025 1
 
0.1%
0.02564102564 1
 
0.1%
0.02597402597 1
 
0.1%
0.02666666667 1
 
0.1%
0.02739726027 1
 
0.1%
ValueCountFrequency (%)
0.9914529915 1
0.1%
0.9908256881 1
0.1%
0.9894736842 1
0.1%
0.987654321 1
0.1%
0.9868421053 1
0.1%
0.9866666667 1
0.1%
0.9848484848 1
0.1%
0.9830508475 1
0.1%
0.8867924528 1
0.1%
0.8260869565 1
0.1%

abandona2_p
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct764
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23994968
Minimum0
Maximum0.98591549
Zeros154
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2023-07-29T15:54:03.265179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.16666667
median0.23943662
Q30.31676443
95-th percentile0.44112662
Maximum0.98591549
Range0.98591549
Interquartile range (IQR)0.15009777

Descriptive statistics

Standard deviation0.12700558
Coefficient of variation (CV)0.52930089
Kurtosis1.2427439
Mean0.23994968
Median Absolute Deviation (MAD)0.075502193
Skewness0.22221929
Sum423.03128
Variance0.016130416
MonotonicityNot monotonic
2023-07-29T15:54:03.411309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 154
 
8.7%
0.25 34
 
1.9%
0.3333333333 30
 
1.7%
0.2 25
 
1.4%
0.2307692308 20
 
1.1%
0.2857142857 19
 
1.1%
0.2727272727 17
 
1.0%
0.1428571429 14
 
0.8%
0.2222222222 14
 
0.8%
0.3043478261 12
 
0.7%
Other values (754) 1424
80.8%
ValueCountFrequency (%)
0 154
8.7%
0.01298701299 1
 
0.1%
0.01515151515 1
 
0.1%
0.02040816327 1
 
0.1%
0.02173913043 1
 
0.1%
0.02272727273 1
 
0.1%
0.02631578947 2
 
0.1%
0.02702702703 1
 
0.1%
0.02777777778 1
 
0.1%
0.0303030303 1
 
0.1%
ValueCountFrequency (%)
0.985915493 1
0.1%
0.9642857143 1
0.1%
0.7073170732 1
0.1%
0.6896551724 1
0.1%
0.6764705882 2
0.1%
0.65 2
0.1%
0.6428571429 1
0.1%
0.6 1
0.1%
0.5897435897 1
0.1%
0.5869565217 1
0.1%

valido1
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.5 KiB
1
1763 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1763
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1763
100.0%

Length

2023-07-29T15:54:03.540203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-29T15:54:03.655172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1763
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1763
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1763
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1763
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1763
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1763
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1763
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1763
100.0%

valido2
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.5 KiB
1
1763 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1763
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1763
100.0%

Length

2023-07-29T15:54:03.744144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-29T15:54:03.859353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1763
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1763
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1763
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1763
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1763
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1763
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1763
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1763
100.0%

aprueba_p
Real number (ℝ)

Distinct1134
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32316837
Minimum0
Maximum1
Zeros4
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2023-07-29T15:54:03.975262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11868442
Q10.22222222
median0.30612245
Q30.40462814
95-th percentile0.56866512
Maximum1
Range1
Interquartile range (IQR)0.18240592

Descriptive statistics

Standard deviation0.14487611
Coefficient of variation (CV)0.44829917
Kurtosis1.5167808
Mean0.32316837
Median Absolute Deviation (MAD)0.091467912
Skewness0.83068928
Sum569.74584
Variance0.020989088
MonotonicityNot monotonic
2023-07-29T15:54:04.119617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.25 19
 
1.1%
0.3333333333 19
 
1.1%
0.5 15
 
0.9%
0.2857142857 12
 
0.7%
0.2222222222 9
 
0.5%
0.3571428571 9
 
0.5%
0.3043478261 8
 
0.5%
0.4 8
 
0.5%
0.3 7
 
0.4%
0.2 7
 
0.4%
Other values (1124) 1650
93.6%
ValueCountFrequency (%)
0 4
0.2%
0.008547008547 1
 
0.1%
0.01052631579 1
 
0.1%
0.01234567901 1
 
0.1%
0.0125 1
 
0.1%
0.01315789474 1
 
0.1%
0.01694915254 1
 
0.1%
0.02479338843 1
 
0.1%
0.0306122449 1
 
0.1%
0.03125 1
 
0.1%
ValueCountFrequency (%)
1 4
0.2%
0.9090909091 1
 
0.1%
0.875 1
 
0.1%
0.8737864078 1
 
0.1%
0.862745098 1
 
0.1%
0.8333333333 1
 
0.1%
0.8311688312 1
 
0.1%
0.8196721311 1
 
0.1%
0.8082191781 1
 
0.1%
0.8035714286 1
 
0.1%

aprueba_rel_p
Real number (ℝ)

Distinct917
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47275702
Minimum0
Maximum1
Zeros4
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2023-07-29T15:54:04.267559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.23333333
Q10.36538462
median0.46666667
Q30.57297519
95-th percentile0.74276498
Maximum1
Range1
Interquartile range (IQR)0.20759057

Descriptive statistics

Standard deviation0.159202
Coefficient of variation (CV)0.33675226
Kurtosis0.59596259
Mean0.47275702
Median Absolute Deviation (MAD)0.1047619
Skewness0.35061357
Sum833.47062
Variance0.025345275
MonotonicityNot monotonic
2023-07-29T15:54:04.414603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 52
 
2.9%
0.3333333333 20
 
1.1%
0.6666666667 19
 
1.1%
0.4285714286 17
 
1.0%
0.4 16
 
0.9%
0.6 14
 
0.8%
1 14
 
0.8%
0.4545454545 14
 
0.8%
0.2857142857 13
 
0.7%
0.5714285714 12
 
0.7%
Other values (907) 1572
89.2%
ValueCountFrequency (%)
0 4
0.2%
0.0243902439 1
 
0.1%
0.03571428571 1
 
0.1%
0.04545454545 1
 
0.1%
0.05555555556 1
 
0.1%
0.0652173913 1
 
0.1%
0.07792207792 1
 
0.1%
0.08928571429 1
 
0.1%
0.09090909091 2
0.1%
0.09375 1
 
0.1%
ValueCountFrequency (%)
1 14
0.8%
0.9565217391 1
 
0.1%
0.9473684211 1
 
0.1%
0.9411764706 1
 
0.1%
0.9333333333 1
 
0.1%
0.9130434783 1
 
0.1%
0.9104477612 1
 
0.1%
0.9090909091 1
 
0.1%
0.9 2
 
0.1%
0.8979591837 1
 
0.1%

Interactions

2023-07-29T15:53:56.372554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:28.589070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:31.181619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:33.437266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:35.393095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:37.314646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:39.110784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:41.104659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:42.977404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:44.842408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:46.865159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:48.778470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:50.605814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:52.434546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:54.572365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:56.503214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:28.727543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:31.346277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:33.566725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:35.534187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:37.443788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:39.242233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:41.240347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-07-29T15:53:44.987052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-07-29T15:53:52.316466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:54.459214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-29T15:53:56.262789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-07-29T15:54:04.548961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
anioSEDEcurson_alump_extp_recursasalapa1_prompa2_promfinal_promprom_edadabandona1_pabandona2_paprueba_paprueba_rel_pcuatMATERIAturno
anio1.000-0.0150.9930.4640.1730.1710.124-0.317-0.112-0.096-0.4790.3590.241-0.350-0.2650.0970.1460.000
SEDE-0.0151.0000.006-0.152-0.461-0.0490.4640.0960.047-0.056-0.060-0.021-0.0770.0850.0930.2500.3190.205
curso0.9930.0061.0000.4560.1400.1440.190-0.306-0.086-0.083-0.5010.3340.221-0.323-0.2410.1600.1750.000
n_alum0.464-0.1520.4561.0000.2820.080-0.073-0.140-0.0220.032-0.3050.0670.095-0.079-0.1050.0930.1910.121
p_ext0.173-0.4610.1400.2821.0000.248-0.559-0.103-0.1390.0950.1450.1900.134-0.193-0.1410.1250.1920.079
p_recursa0.171-0.0490.1440.0800.2481.000-0.269-0.068-0.017-0.1720.2910.1930.184-0.142-0.0820.1810.1900.267
sala0.1240.4640.190-0.073-0.559-0.2691.0000.0000.163-0.110-0.280-0.114-0.0630.1100.0670.2350.2040.135
pa1_prom-0.3170.096-0.306-0.140-0.103-0.0680.0001.0000.442-0.1160.052-0.134-0.6120.6230.8200.1030.2740.072
pa2_prom-0.1120.047-0.086-0.022-0.139-0.0170.1630.4421.000-0.012-0.062-0.256-0.2790.6150.6840.1650.0820.000
final_prom-0.096-0.056-0.0830.0320.095-0.172-0.110-0.116-0.0121.000-0.134-0.191-0.0420.063-0.0390.1860.0370.127
prom_edad-0.479-0.060-0.501-0.3050.1450.291-0.2800.052-0.062-0.1341.0000.2530.162-0.183-0.0420.2390.1130.332
abandona1_p0.359-0.0210.3340.0670.1900.193-0.114-0.134-0.256-0.1910.2531.0000.275-0.717-0.2580.1870.2190.288
abandona2_p0.241-0.0770.2210.0950.1340.184-0.063-0.612-0.279-0.0420.1620.2751.000-0.590-0.7110.1520.0820.170
aprueba_p-0.3500.085-0.323-0.079-0.193-0.1420.1100.6230.6150.063-0.183-0.717-0.5901.0000.8040.2140.1200.185
aprueba_rel_p-0.2650.093-0.241-0.105-0.141-0.0820.0670.8200.684-0.039-0.042-0.258-0.7110.8041.0000.1990.0970.053
cuat0.0970.2500.1600.0930.1250.1810.2350.1030.1650.1860.2390.1870.1520.2140.1991.0000.1590.030
MATERIA0.1460.3190.1750.1910.1920.1900.2040.2740.0820.0370.1130.2190.0820.1200.0970.1591.0000.072
turno0.0000.2050.0000.1210.0790.2670.1350.0720.0000.1270.3320.2880.1700.1850.0530.0300.0721.000

Missing values

2023-07-29T15:53:58.238482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-29T15:53:58.562541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

aniocuatSEDEMATERIAcursoturnon_alump_extp_recursasalapa1_prompa2_promfinal_promprom_edadabandona1_pabandona2_pvalido1valido2aprueba_paprueba_rel_p
020111130A710.0704230.0103.3409093.1428574.1428573.4647890.3802820.363636110.2253520.363636
120111131A580.1206900.0122.5000001.8666674.5000003.4310340.3103450.250000110.1551720.225000
220111132B780.0897440.0103.8727273.6666673.8461543.6538460.2948720.236364110.2948720.418182
320111133B750.1733330.0123.5102044.0263164.0769233.4533330.3466670.224490110.2666670.408163
420111134C420.1666670.0103.5333333.5500003.6250003.6904760.2857140.333333110.2619050.366667
520111135C680.2058820.0102.6451614.5000004.6666673.8823530.5441180.548387110.1176470.258065
620111136C590.0847460.0122.3548392.8235296.5000004.1355930.4745760.451613110.0847460.161290
720111137D830.1325300.0102.4615383.2500004.3333334.2168670.5301200.589744110.1204820.256410
820111138D590.1525420.0123.3448283.7333333.8333334.1186440.5084750.482759110.1525420.310345
920111139A600.2000000.034.1190483.8125003.0000003.3166670.3000000.238095110.2666670.380952
aniocuatSEDEMATERIAcursoturnon_alump_extp_recursasalapa1_prompa2_promfinal_promprom_edadabandona1_pabandona2_pvalido1valido2aprueba_paprueba_rel_p
2648201925532581A840.1785710.047619623.1538463.3684215.2000002.7738100.3809520.269231110.2380950.384615
26492019221532634B730.0410960.2602741202.7343753.9512204.7222222.6712330.1232880.359375110.3150680.359375
2652201922532543C1040.1923080.471154153.0000003.5333334.0952383.0192310.3269230.357143110.2596150.385714
265520192232529A1330.0601500.015038143.3707874.9830513.9000002.6165410.3308270.337079110.3082710.460674
2656201922532546A1250.4480000.248000183.0405413.6181825.2307692.7600000.3920000.276316110.2320000.381579
2658201923132642A190.0000000.1052631393.5333333.5000003.8000002.6842110.2105260.200000110.3684210.466667
26592019228532637B400.0250000.4250001333.2758625.4000004.0000002.7500000.2750000.310345110.3500000.482759
26612019230532641B340.0000000.0588241385.4250005.2777782.6666672.5588240.4117650.100000110.4411760.750000
2662201922732636B50.0000000.4000001256.0000005.3333333.5000002.6000000.4000000.000000110.6000001.000000
2663201923132643A10.0000000.0000001395.0000005.0000003.0000002.0000000.0000000.000000111.0000001.000000